Learned World Models for ML Experiment Search

MLE-World uses a learned predictor to screen ML experiments before costly execution, reporting 77.9% held-out output matching on 17 MLE-bench Lite tasks and gains in guided search. A related report claims uncertainty-based gating reduces real execution time 30–40%; broader validation, calibration, and reproducible artifacts are still needed.